Chemical interrogation of the malaria kinome.

Malaria, an infectious disease caused by eukaryotic parasites of the genus Plasmodium, afflicts hundreds of millions of people every year. Both the parasite and its host utilize protein kinases to regulate essential cellular processes. Bioinformatic analyses of parasite genomes predict at least 65 protein kinases, but their biological functions and therapeutic potential are largely unknown. We profiled 1358 small-molecule kinase inhibitors to evaluate the role of both the human and the malaria kinomes in Plasmodium infection of liver cells, the parasites' obligatory but transient developmental stage that precedes the symptomatic blood stage. The screen identified several small molecules that inhibit parasite load in liver cells, some with nanomolar efficacy, and each compound was subsequently assessed for activity against blood-stage malaria. Most of the screening hits inhibited both liver- and blood-stage malaria parasites, which have dissimilar gene expression profiles and infect different host cells. Evaluation of existing kinase activity profiling data for the library members suggests that several kinases are essential to malaria parasites, including cyclin-dependent kinases (CDKs), glycogen synthase kinases, and phosphoinositide-3-kinases. CDK inhibitors were found to bind to Plasmodium protein kinase 5, but it is likely that these compounds target multiple parasite kinases. The dual-stage inhibition of the identified kinase inhibitors makes them useful chemical probes and promising starting points for antimalarial development.

Collaboration gets the most out of software.

By centralizing many of the tasks associated with the upkeep of scientific software, SBGrid allows researchers to spend more of their time on research.

Improving database enrichment through ensemble docking.

While it may seem intuitive that using an ensemble of multiple conformations of a receptor in structure-based virtual screening experiments would necessarily yield improved enrichment of actives relative to using just a single receptor, it turns out that at least in the p38 MAP kinase model system studied here, a very large majority of all possible ensembles do not yield improved enrichment of actives. However, there are combinations of receptor structures that do lead to improved enrichment results. We present here a method to select the ensembles that produce the best enrichments that does not rely on knowledge of active compounds or sophisticated analyses of the 3D receptor structures. In the system studied here, the small fraction of ensembles of up to 3 receptors that do yield good enrichments of actives were identified by selecting ensembles that have the best mean GlideScore for the top 1% of the docked ligands in a database screen of actives and drug-like "decoy" ligands. Ensembles of two receptors identified using this mean GlideScore metric generally outperform single receptors, while ensembles of three receptors identified using this metric consistently give optimal enrichment factors in which, for example, 40% of the known actives outrank all the other ligands in the database.

Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes.

A novel scoring function to estimate protein-ligand binding affinities has been developed and implemented as the Glide 4.0 XP scoring function and docking protocol. In addition to unique water desolvation energy terms, protein-ligand structural motifs leading to enhanced binding affinity are included: (1) hydrophobic enclosure where groups of lipophilic ligand atoms are enclosed on opposite faces by lipophilic protein atoms, (2) neutral-neutral single or correlated hydrogen bonds in a hydrophobically enclosed environment, and (3) five categories of charged-charged hydrogen bonds. The XP scoring function and docking protocol have been developed to reproduce experimental binding affinities for a set of 198 complexes (RMSDs of 2.26 and 1.73 kcal/mol over all and well-docked ligands, respectively) and to yield quality enrichments for a set of fifteen screens of pharmaceutical importance. Enrichment results demonstrate the importance of the novel XP molecular recognition and water scoring in separating active and inactive ligands and avoiding false positives.

Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening.

For the successful identification and docking of new ligands to a protein target by virtual screening, the essential features of the protein and ligand surfaces must be captured and distilled in an efficient representation. Since the running time for docking increases exponentially with the number of points representing the protein and each ligand candidate, it is important to place these points where the best interactions can be made between the protein and the ligand. This definition of favorable points of interaction can also guide protein structure-based ligand design, which typically focuses on which chemical groups provide the most energetically favorable contacts. In this paper, we present an alternative method of protein template and ligand interaction point design that identifies the most favorable points for making hydrophobic and hydrogen-bond interactions by using a knowledge base. The knowledge-based protein and ligand representations have been incorporated in version 2.0 of SLIDE and resulted in dockings closer to the crystal structure orientations when screening a set of 57 known thrombin and glutathione S-transferase (GST) ligands against the apo structures of these proteins. There was also improved scoring enrichment of the dockings, meaning better differentiation between the chemically diverse known ligands and a approximately 15,000-molecule dataset of randomly-chosen small organic molecules. This approach for identifying the most important points of interaction between proteins and their ligands can equally well be used in other docking and design techniques. While much recent effort has focused on improving scoring functions for protein-ligand docking, our results indicate that improving the representation of the chemistry of proteins and their ligands is another avenue that can lead to significant improvements in the identification, docking, and scoring of ligands.